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Flexible Mx Specification of Various Extended Twin Kinship Designs

Published online by Cambridge University Press:  21 February 2012

Hermine H. Maes*
Affiliation:
Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia, United States of America; Massey Cancer Center, Virginia Commonwealth University, Richmond, Virginia, United States of America. [email protected]
Michael C. Neale
Affiliation:
Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia, United States of America.
Sarah E. Medland
Affiliation:
Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia, United States of America.
Matthew C. Keller
Affiliation:
Department of Psychology, University of Colorado at Boulder, Boulder, Colorado, United States of America.
Nicholas G. Martin
Affiliation:
Queensland Institute of Medical Research, Brisbane, Australia.
Andrew C. Heath
Affiliation:
Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri, United States of America.
Lindon J. Eaves
Affiliation:
Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia, United States of America.
*
*Address for correspondence: Hermine H. Maes, Virginia Institute for Psychiatric and Behavioral Genetics, PO Box 980003, Richmond VA, 23298-0003, USA.

Abstract

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The extended twin kinship design allows the simultaneous testing of additive and nonadditive genetic, shared and individual-specific environmental factors, as well as sex differences in the expression of genes and environment in the presence of assortative mating and combined genetic and cultural transmission (Eaves et al., 1999). It also handles the contribution of these sources of variance to the (co)variation of multiple phenotypes. Keller et al. (2008) extended this comprehensive model for family resemblance to allow or a flexible specification of assortment and vertical transmission. As such, it provides a general framework which can easily be reduced to fit subsets of data such as twin-parent data, children-of-twins data, etc. A flexible Mx specification of this model that allows handling of these various designs is presented in detail and applied to data from the Virginia 30,000. Data on height, body mass index, smoking status, church attendance, and political affiliation were obtained from twins and their families. Results indicate that biases in the estimation of variance components depend both on the types of relative available for analysis, and on the underlying genetic and environmental architecture of the phenotype of interest.

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Articles
Copyright
Copyright © Cambridge University Press 2009